Blended Probabilistic Tornado Forecasts: Combining Climatological Frequencies with NSSL–WRF Ensemble Forecasts

Abstract

Abstract Attempts at probabilistic tornado forecasting using convection-allowing models (CAMs) have thus far used CAM attribute [e.g., hourly maximum 2–5-km updraft helicity (UH)] thresholds, treating them as binary events—either a grid point exceeds a given threshold or it does not. This study approaches these attributes probabilistically, using empirical observations of storm environment attributes and the subsequent climatological tornado occurrence frequency to assign a probability that a point will be within 40 km of a tornado, given the model-derived storm environment attributes. Combining empirical frequencies and forecast attributes produces better forecasts than solely using mid- or low-level UH, even if the UH is filtered using environmental parameter thresholds. Empirical tornado frequencies were derived using severe right-moving supercellular storms associated with a local storm report (LSR) of a tornado, severe wind, or severe hail for a given significant tornado parameter (STP) value from Storm Prediction Center (SPC) mesoanalysis grids in 2014–15. The NSSL–WRF ensemble produced the forecast STP values and simulated right-moving supercells, which were identified using a UH exceedance threshold. Model-derived probabilities are verified using tornado segment data from just right-moving supercells and from all tornadoes, as are the SPC-issued 0600 UTC tornado probabilities from the initial day 1 forecast valid 1200–1159 UTC the following day. The STP-based probabilistic forecasts perform comparably to SPC tornado probability forecasts in many skill metrics (e.g., reliability) and thus could be used as first-guess forecasts. Comparison with prior methodologies shows that probabilistic environmental information improves CAM-based tornado forecasts.